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About Business Analytics 3.0

How can firms achieve positive returns on their analytics investments by taking advantage of the growing amounts of data? If you’re an executive, manager, or team leader, one of your toughest responsibilities is managing and organizing your analytics initiative to create value.

Analytics are integral to the business operations. Here are just a few examples of analytics at work:

In every industry, senior leaders wonder whether they are getting full value from the massive amounts of information their organizations already have. New technologies collect more data better, faster and cheaper than ever before, yet many firms struggle to obtain value from their data because of fragmentation.

Knowing what happened (reporting) and why it happened (descriptive analytics) are no longer adequate. Market leaders need to know what is happening now, what is likely to happen next and, what actions they should take for optimal results. Business analytics must help firms recognize subtle trends and patterns so they can anticipate and shape events and improve outcomes. Not only drive more top-line growth and control costs, but also identify risks – and take timely corrective action.

Analytics based data products and decision processes is the new wave.

Do you have the right toolset, dataset, skillset and mindset for execution?

BI and Analytics is now the top priority in most corporations according the Gartner Survey. Studies show that organizations that apply analytics outperform their peers. And those with a high “Analytics Quotient” – that is, a broad-based, analytics-driven culture – perform, on average, three times better.

In the Information Age, there is an unprecedented amount of data being collected and stored — by banks, retailers, supermarkets, internet sites, governments, credit card firms, etc. So, now that we have all this data, what do we with it? How do we process it?

Business Analytics 3.0 blog is meant for decision makers, managers and technologists who are trying to make sense of the rapidly changing data technology landscape and build next generation solutions.

Design: Make data matter with highly effective user experiences, using new interfaces, interactivity, and visualization.

Big Data in Practice: Get practical lessons, integration tricks, and a glimpse of the road ahead.

Governance: Understand issues in compliance, governance, and leadership in the era of data.

Directional Trend: Data, Data and More Data

The directional trend is unstoppable. The research firm IDC predicts that global data volumes will increase by 29 times over the next 10 years to 35 zettabytes. (A zettabyte is a 1 followed by 21 zeros.) The incredible volume of data and the increased complexity of data management are driving demand for new “insight” services.

Given the growing volume of data, there is paradigm shift taking place in analytics: moving from “capture-and-mine-data” models to near real-time pattern and signal identification. We are moving from “sense and respond” to “anticipate and shape” models.

Take for instance, web and mobile e-commerce. Consumers create enormous trails of data as they surf, browse, buy, share, and search across the Web. There are now large stores of information about history and intent, preferences and choice, products and services, which can be captured and analyzed.

With enterprises needing a way to manage and mine potentially valuable information from this big data streams, advanced data analytics that separates “signals-from-noise” is witnessing increased adoption. Companies have to leverage this data fire hose to design interactions, processes, experiences, and products that will better match customer needs.

How to guide management strategy in the most profitable directions with timely, reliable insights, scenario modeling and transparent and timely reporting.

How to discover subtle patterns and associations and develop and deploy predictive models to optimize decision-making.

How to gain deep insight into all aspects of risk management, including governance, risk and compliance, with an integrated solution that adapts to your organization’s unique risk profile and methodology’s.

How to provide line-of-business managers with actionable insights through packaged analysis and reporting solutions.

Directional Trend: Analytics is Moving from Fringe to Core

So, what’s next? We are beginning to see companies assemble new reference architectures for building next gen analytics platforms. The focus today is at the real-time Systems of Engagement (or customer interface) layer – offerings sit between traditional IT systems and a complex array of channels and devices that customers are using to access their services today.

However, there is an art and science to developing Business Analytics 3.0 solutions.

The WHAT of building new and unique “data” experiences starts with design thinking (Art). Envisioning and building the analytics architecture for generating and presenting novel insights is the baseline proposition for any analytics solution.

The HOW is achieved through a variety of measurement, optimization and visualization tools that form the tiles of a industrial strength analytics platform (Science).

We are on the cusp of a remarkable wave of innovation, productivity and growth thanks to new and emerging data integration, analysis and visualization technologies. Enterprise companies that are seeking a competitive advantage will embrace this shift.

Analytics (and big data) touches everyone in the modern world. We are now transitioning to a multi-functional discipline which will define the next decade. A great opportunity resides in transforming the current system landscape into a system of differentiation for business.

Directional Trend: World-class Business Analytics

Despite all the excitement, the journey to world-class analytics is going to be a major transformation exercise. CIOs, everywhere, are faced with the daunting task of unlocking the value of their data efficiently in the time-frame required to enable accurate decisions.

Hope you are as excited about this topic as we are. We hope to share our insights and interacting with you in this blog.

Defining Business Analytics

What is Business Analytics? Business Analytics is the intersection of business and technology, offering new opportunities for a competitive advantage. Business analytics unlocks the predictive potential of data analysis to improve financial performance, strategic management, and operational efficiency.

What is BI? BI is the "computer-based techniques used in spotting, digging-out, and analyzing 'hard' business data, such as sales revenue by products or departments or associated costs and incomes. Objectives of BI implementations include (1) understanding of a firm's internal and external strengths and weaknesses, (2) understanding of the relationship between different data for better decision making, (3) detection of opportunities for innovation, and (4) cost reduction and optimal deployment of resources." (Business Dictionary). Most widely used BI tool is Microsoft Excel.
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What is Big Data? Big data refer to data scenarios that grow so large (petabytes and more) that they become awkward to work with using traditional database management tools. The challenge stems from data volume + flow velocity + noise to signal conversion. Big data is spawning new tools that are mix of significant processing power, parallelism and statistical, machine learning, or pattern recognition techniques
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Corporate performance management software and performance management concepts, such as the balanced scorecard, enable organizations to measure business results and track their progress against business goals in order to improve financial performance.
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Data visualization tools, include mashups, executive dashboards, performance scorecards and other data visualization technology, is becoming a major category.
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BI platforms provide a range of capabilities for building analytical applications. Examples are Oracle OBIEE, SAP Business Objects 4.0. There are many choices and combinations of BI platforms, capabilities and use cases as well as many emerging BI technologies such as in memory analytics, interactive visualization and BI integrated search. The idea of standardizing on one supplier for all of one’s BI capabilities is difficult to do. Increasingly, standardization and more about managing a portfolio of tools used for a set of capabilities and use cases.
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Data integration tools and architectures in support of BI continue to evolve. Extract-Transfer-Load (ETL) tools make up a big segment of this category in addition to data mapping tools. Organizations must now support a range of delivery styles, latencies, and formats.
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BI is about "sense and respond." Analytics is about "anticipate and shape" models.

About

Business Analytics 3.0 blog is meant for decision makers and managers who are trying to make sense of the rapidly changing technology landscape and build next generation solutions. It is aimed at helping business decision makers navigate the "Raw Data -> Aggregate Data -> Intelligence -> Insight -> Decisions" chain.